Learning Approaches to Wrapper Induction
Gunter Grieser, Steffen Lange · 2001
The number, the size, and the dynamics of lntemet in-formation sources bears abundant evidence of the need of automation in information extraction (IE). This paper deals with the question of how such extraction mecha-nisms can automatically be created by invoking learning techniques. The underlying scenario of system-supported IE is putting certain constraints on the available training ex-amples. Therefore, the traditional approaches to formal language learning do not capture the kind of problems to be solved when learning the corresponding extraction mechanisms. We illustrate the resulting differences by studying the problem of learning a particular type of extraction mechanisms ( o-called island wrappers). We show how to decompose this learning problem into different sub-problems that can be handled independently and in par-allel. Moreover, we relate the learning problems on hand to the problems that learning theory papers orig-inally address and point out what they have in common and where the differences are. Motivation